Overview

Dataset statistics

Number of variables12
Number of observations205
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory19.3 KiB
Average record size in memory96.6 B

Variable types

Categorical1
Numeric11

Alerts

name has a high cardinality: 147 distinct values High cardinality
wheelbase is highly correlated with carlength and 7 other fieldsHigh correlation
carlength is highly correlated with wheelbase and 8 other fieldsHigh correlation
carwidth is highly correlated with wheelbase and 8 other fieldsHigh correlation
curbweight is highly correlated with wheelbase and 9 other fieldsHigh correlation
cylindernumber is highly correlated with curbweight and 5 other fieldsHigh correlation
enginesize is highly correlated with wheelbase and 9 other fieldsHigh correlation
boreratio is highly correlated with wheelbase and 8 other fieldsHigh correlation
horsepower is highly correlated with wheelbase and 9 other fieldsHigh correlation
citympg is highly correlated with carlength and 8 other fieldsHigh correlation
highwaympg is highly correlated with wheelbase and 9 other fieldsHigh correlation
price is highly correlated with wheelbase and 9 other fieldsHigh correlation
wheelbase is highly correlated with carlength and 5 other fieldsHigh correlation
carlength is highly correlated with wheelbase and 8 other fieldsHigh correlation
carwidth is highly correlated with wheelbase and 9 other fieldsHigh correlation
curbweight is highly correlated with wheelbase and 9 other fieldsHigh correlation
cylindernumber is highly correlated with carwidth and 4 other fieldsHigh correlation
enginesize is highly correlated with wheelbase and 9 other fieldsHigh correlation
boreratio is highly correlated with carlength and 7 other fieldsHigh correlation
horsepower is highly correlated with carlength and 8 other fieldsHigh correlation
citympg is highly correlated with carlength and 7 other fieldsHigh correlation
highwaympg is highly correlated with wheelbase and 8 other fieldsHigh correlation
price is highly correlated with wheelbase and 9 other fieldsHigh correlation
wheelbase is highly correlated with carlength and 3 other fieldsHigh correlation
carlength is highly correlated with wheelbase and 6 other fieldsHigh correlation
carwidth is highly correlated with wheelbase and 6 other fieldsHigh correlation
curbweight is highly correlated with wheelbase and 7 other fieldsHigh correlation
enginesize is highly correlated with carlength and 6 other fieldsHigh correlation
horsepower is highly correlated with curbweight and 4 other fieldsHigh correlation
citympg is highly correlated with carlength and 6 other fieldsHigh correlation
highwaympg is highly correlated with carlength and 6 other fieldsHigh correlation
price is highly correlated with wheelbase and 7 other fieldsHigh correlation
wheelbase is highly correlated with carlength and 9 other fieldsHigh correlation
carlength is highly correlated with wheelbase and 9 other fieldsHigh correlation
carwidth is highly correlated with wheelbase and 9 other fieldsHigh correlation
curbweight is highly correlated with wheelbase and 9 other fieldsHigh correlation
cylindernumber is highly correlated with wheelbase and 9 other fieldsHigh correlation
enginesize is highly correlated with wheelbase and 9 other fieldsHigh correlation
boreratio is highly correlated with wheelbase and 9 other fieldsHigh correlation
horsepower is highly correlated with wheelbase and 9 other fieldsHigh correlation
citympg is highly correlated with wheelbase and 9 other fieldsHigh correlation
highwaympg is highly correlated with wheelbase and 9 other fieldsHigh correlation
price is highly correlated with wheelbase and 9 other fieldsHigh correlation
name is uniformly distributed Uniform
cylindernumber has 4 (2.0%) zeros Zeros

Reproduction

Analysis started2022-09-02 00:03:08.965825
Analysis finished2022-09-02 00:03:41.528629
Duration32.56 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct147
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
toyota corona
 
6
toyota corolla
 
6
peugeot 504
 
6
subaru dl
 
4
mitsubishi mirage g4
 
3
Other values (142)
180 

Length

Max length31
Median length24
Mean length14.14634146
Min length6

Characters and Unicode

Total characters2900
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)53.2%

Sample

1st rowalfa-romero giulia
2nd rowalfa-romero stelvio
3rd rowalfa-romero Quadrifoglio
4th rowaudi 100 ls
5th rowaudi 100ls

Common Values

ValueCountFrequency (%)
toyota corona6
 
2.9%
toyota corolla6
 
2.9%
peugeot 5046
 
2.9%
subaru dl4
 
2.0%
mitsubishi mirage g43
 
1.5%
mazda 6263
 
1.5%
toyota mark ii3
 
1.5%
mitsubishi outlander3
 
1.5%
mitsubishi g43
 
1.5%
honda civic3
 
1.5%
Other values (137)165
80.5%

Length

2022-09-01T21:03:41.836765image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota31
 
6.4%
nissan18
 
3.7%
mazda15
 
3.1%
mitsubishi13
 
2.7%
honda13
 
2.7%
corolla12
 
2.5%
subaru12
 
2.5%
peugeot11
 
2.3%
volvo11
 
2.3%
sw10
 
2.0%
Other values (167)342
70.1%

Most occurring characters

ValueCountFrequency (%)
285
 
9.8%
a259
 
8.9%
o243
 
8.4%
t167
 
5.8%
e158
 
5.4%
s153
 
5.3%
i147
 
5.1%
l138
 
4.8%
r133
 
4.6%
c126
 
4.3%
Other values (36)1091
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2384
82.2%
Space Separator285
 
9.8%
Decimal Number179
 
6.2%
Close Punctuation13
 
0.4%
Dash Punctuation13
 
0.4%
Open Punctuation13
 
0.4%
Uppercase Letter13
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a259
 
10.9%
o243
 
10.2%
t167
 
7.0%
e158
 
6.6%
s153
 
6.4%
i147
 
6.2%
l138
 
5.8%
r133
 
5.6%
c126
 
5.3%
u126
 
5.3%
Other values (15)734
30.8%
Decimal Number
ValueCountFrequency (%)
044
24.6%
437
20.7%
123
12.8%
221
11.7%
518
10.1%
912
 
6.7%
612
 
6.7%
310
 
5.6%
72
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
M4
30.8%
D3
23.1%
U1
 
7.7%
X1
 
7.7%
Q1
 
7.7%
V1
 
7.7%
C1
 
7.7%
N1
 
7.7%
Space Separator
ValueCountFrequency (%)
285
100.0%
Close Punctuation
ValueCountFrequency (%)
)13
100.0%
Dash Punctuation
ValueCountFrequency (%)
-13
100.0%
Open Punctuation
ValueCountFrequency (%)
(13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2397
82.7%
Common503
 
17.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a259
 
10.8%
o243
 
10.1%
t167
 
7.0%
e158
 
6.6%
s153
 
6.4%
i147
 
6.1%
l138
 
5.8%
r133
 
5.5%
c126
 
5.3%
u126
 
5.3%
Other values (23)747
31.2%
Common
ValueCountFrequency (%)
285
56.7%
044
 
8.7%
437
 
7.4%
123
 
4.6%
221
 
4.2%
518
 
3.6%
)13
 
2.6%
-13
 
2.6%
(13
 
2.6%
912
 
2.4%
Other values (3)24
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII2900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
285
 
9.8%
a259
 
8.9%
o243
 
8.4%
t167
 
5.8%
e158
 
5.4%
s153
 
5.3%
i147
 
5.1%
l138
 
4.8%
r133
 
4.6%
c126
 
4.3%
Other values (36)1091
37.6%

wheelbase
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct53
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3544193984
Minimum0
Maximum1
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:42.040527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1871720117
Q10.2303206997
median0.3032069971
Q30.4606413994
95-th percentile0.6822157434
Maximum1
Range1
Interquartile range (IQR)0.2303206997

Descriptive statistics

Standard deviation0.1755619733
Coefficient of variation (CV)0.4953509151
Kurtosis1.017038946
Mean0.3544193984
Median Absolute Deviation (MAD)0.07871720117
Skewness1.050213776
Sum72.65597668
Variance0.03082200648
MonotonicityNot monotonic
2022-09-01T21:03:42.274898image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.230320699721
 
10.2%
0.206997084520
 
9.8%
0.265306122413
 
6.3%
0.28862973768
 
3.9%
0.31195335287
 
3.4%
0.34402332367
 
3.4%
0.51603498546
 
2.9%
0.40233236156
 
2.9%
0.62099125366
 
2.9%
0.35568513126
 
2.9%
Other values (43)105
51.2%
ValueCountFrequency (%)
02
 
1.0%
0.052478134111
 
0.5%
0.05830903792
 
1.0%
0.084548104963
 
1.5%
0.13702623912
 
1.0%
0.18658892131
 
0.5%
0.18950437325
 
2.4%
0.1953352771
 
0.5%
0.206997084520
9.8%
0.22448979591
 
0.5%
ValueCountFrequency (%)
11
 
0.5%
0.84548104962
 
1.0%
0.8046647234
2.0%
0.76967930032
 
1.0%
0.74052478131
 
0.5%
0.68221574343
1.5%
0.65597667645
2.4%
0.62390670551
 
0.5%
0.62099125366
2.9%
0.58600583091
 
0.5%

carlength
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct75
Distinct (%)36.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4917801238
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:42.524864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2394029851
Q10.376119403
median0.4791044776
Q30.6268656716
95-th percentile0.8247761194
Maximum1
Range1
Interquartile range (IQR)0.2507462687

Descriptive statistics

Standard deviation0.1841386347
Coefficient of variation (CV)0.3744328529
Kurtosis-0.08289485345
Mean0.4917801238
Median Absolute Deviation (MAD)0.1029850746
Skewness0.1559537713
Sum100.8149254
Variance0.0339070368
MonotonicityNot monotonic
2022-09-01T21:03:42.751514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.241791044815
 
7.3%
0.711940298511
 
5.4%
0.45671641797
 
3.4%
0.68059701497
 
3.4%
0.3761194037
 
3.4%
0.36119402996
 
2.9%
0.5477611946
 
2.9%
0.5238805976
 
2.9%
0.67910447766
 
2.9%
0.46119402995
 
2.4%
Other values (65)129
62.9%
ValueCountFrequency (%)
01
 
0.5%
0.052238805972
 
1.0%
0.13283582093
 
1.5%
0.22089552243
 
1.5%
0.23582089551
 
0.5%
0.23880597011
 
0.5%
0.241791044815
7.3%
0.25074626871
 
0.5%
0.26268656723
 
1.5%
0.26417910451
 
0.5%
ValueCountFrequency (%)
11
 
0.5%
0.91791044782
1.0%
0.87313432842
1.0%
0.86716417911
 
0.5%
0.86268656724
2.0%
0.83432835821
 
0.5%
0.78656716421
 
0.5%
0.77014925373
1.5%
0.75522388061
 
0.5%
0.74328358212
1.0%

carwidth
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4673170732
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:43.047269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.275
Q10.3166666667
median0.4333333333
Q30.55
95-th percentile0.8466666667
Maximum1
Range1
Interquartile range (IQR)0.2333333333

Descriptive statistics

Standard deviation0.1787669877
Coefficient of variation (CV)0.3825389612
Kurtosis0.7027642441
Mean0.4673170732
Median Absolute Deviation (MAD)0.1166666667
Skewness0.9040034988
Sum95.8
Variance0.0319576359
MonotonicityNot monotonic
2022-09-01T21:03:43.312391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.291666666724
 
11.7%
0.516666666723
 
11.2%
0.42515
 
7.3%
0.27511
 
5.4%
0.341666666710
 
4.9%
0.67510
 
4.9%
0.30833333339
 
4.4%
0.43333333338
 
3.9%
0.40833333337
 
3.4%
0.3256
 
2.9%
Other values (34)82
40.0%
ValueCountFrequency (%)
01
 
0.5%
0.1251
 
0.5%
0.18333333331
 
0.5%
0.25833333331
 
0.5%
0.27511
5.4%
0.291666666724
11.7%
0.33
 
1.5%
0.30833333339
 
4.4%
0.31666666672
 
1.0%
0.3256
 
2.9%
ValueCountFrequency (%)
11
 
0.5%
0.9751
 
0.5%
0.953
1.5%
0.9253
1.5%
0.88333333331
 
0.5%
0.85833333331
 
0.5%
0.851
 
0.5%
0.83333333333
1.5%
0.7752
1.0%
0.71666666674
2.0%

curbweight
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct171
Distinct (%)83.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4141062272
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:43.562350image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1602017067
Q10.2548487199
median0.359193173
Q30.56128782
95-th percentile0.781613654
Maximum1
Range1
Interquartile range (IQR)0.3064391001

Descriptive statistics

Standard deviation0.2019705987
Coefficient of variation (CV)0.4877265433
Kurtosis-0.0428537661
Mean0.4141062272
Median Absolute Deviation (MAD)0.1497284717
Skewness0.6813981891
Sum84.89177657
Variance0.04079212275
MonotonicityNot monotonic
2022-09-01T21:03:43.786000image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.34794414274
 
2.0%
0.16679596593
 
1.5%
0.30527540733
 
1.5%
0.19433669513
 
1.5%
0.35764158262
 
1.0%
0.27269200932
 
1.0%
0.4061287822
 
1.0%
0.20791311092
 
1.0%
0.3591931732
 
1.0%
12
 
1.0%
Other values (161)180
87.8%
ValueCountFrequency (%)
01
0.5%
0.087276958881
0.5%
0.1283941041
0.5%
0.13537626071
0.5%
0.14972847172
1.0%
0.15050426692
1.0%
0.15554693561
0.5%
0.15593483321
0.5%
0.15981380921
0.5%
0.16175329711
0.5%
ValueCountFrequency (%)
12
1.0%
0.9550038791
0.5%
0.93560899921
0.5%
0.88518231191
0.5%
0.877424361
0.5%
0.8735453841
0.5%
0.86384794411
0.5%
0.85221101631
0.5%
0.78626842511
0.5%
0.78238944921
0.5%

cylindernumber
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2380487805
Minimum0
Maximum1
Zeros4
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:44.125193image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.2
median0.2
Q30.2
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1080853764
Coefficient of variation (CV)0.4540471754
Kurtosis13.71486634
Mean0.2380487805
Median Absolute Deviation (MAD)0
Skewness2.817459025
Sum48.8
Variance0.01168244859
MonotonicityNot monotonic
2022-09-01T21:03:44.265821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.2159
77.6%
0.424
 
11.7%
0.311
 
5.4%
0.65
 
2.4%
04
 
2.0%
0.11
 
0.5%
11
 
0.5%
ValueCountFrequency (%)
04
 
2.0%
0.11
 
0.5%
0.2159
77.6%
0.311
 
5.4%
0.424
 
11.7%
0.65
 
2.4%
11
 
0.5%
ValueCountFrequency (%)
11
 
0.5%
0.65
 
2.4%
0.424
 
11.7%
0.311
 
5.4%
0.2159
77.6%
0.11
 
0.5%
04
 
2.0%

enginesize
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2487068569
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:44.437691image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1094339623
Q10.1358490566
median0.2226415094
Q30.3018867925
95-th percentile0.5290566038
Maximum1
Range1
Interquartile range (IQR)0.1660377358

Descriptive statistics

Standard deviation0.1571422394
Coefficient of variation (CV)0.6318371812
Kurtosis5.305682092
Mean0.2487068569
Median Absolute Deviation (MAD)0.08679245283
Skewness1.947655045
Sum50.98490566
Variance0.0246936834
MonotonicityNot monotonic
2022-09-01T21:03:44.612720image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.230188679215
 
7.3%
0.116981132115
 
7.3%
0.135849056614
 
6.8%
0.139622641514
 
6.8%
0.177358490613
 
6.3%
0.109433962312
 
5.9%
0.184905660412
 
5.9%
0.18113207558
 
3.9%
0.22264150947
 
3.4%
0.30188679257
 
3.4%
Other values (34)88
42.9%
ValueCountFrequency (%)
01
 
0.5%
0.033962264153
 
1.5%
0.06792452831
 
0.5%
0.071698113211
 
0.5%
0.109433962312
5.9%
0.11320754725
 
2.4%
0.116981132115
7.3%
0.135849056614
6.8%
0.139622641514
6.8%
0.1584905661
 
0.5%
ValueCountFrequency (%)
11
 
0.5%
0.93207547171
 
0.5%
0.91698113211
 
0.5%
0.74339622642
 
1.0%
0.65283018872
 
1.0%
0.5584905663
1.5%
0.53584905661
 
0.5%
0.50188679253
1.5%
0.46037735854
2.0%
0.45283018876
2.9%

boreratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5641114983
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:44.768968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3071428571
Q10.4357142857
median0.55
Q30.7428571429
95-th percentile0.8857142857
Maximum1
Range1
Interquartile range (IQR)0.3071428571

Descriptive statistics

Standard deviation0.1934597896
Coefficient of variation (CV)0.3429460137
Kurtosis-0.7850418332
Mean0.5641114983
Median Absolute Deviation (MAD)0.1857142857
Skewness0.0201564181
Sum115.6428571
Variance0.03742669019
MonotonicityNot monotonic
2022-09-01T21:03:44.925221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.771428571423
 
11.2%
0.464285714320
 
9.8%
0.435714285715
 
7.3%
0.3512
 
5.9%
0.307142857112
 
5.9%
0.65714285719
 
4.4%
0.558
 
3.9%
0.63571428578
 
3.9%
0.88571428578
 
3.9%
0.52142857147
 
3.4%
Other values (28)83
40.5%
ValueCountFrequency (%)
01
 
0.5%
0.11
 
0.5%
0.26428571437
3.4%
0.27142857141
 
0.5%
0.307142857112
5.9%
0.32142857141
 
0.5%
0.33571428575
2.4%
0.3512
5.9%
0.36428571436
2.9%
0.38571428571
 
0.5%
ValueCountFrequency (%)
12
 
1.0%
0.92
 
1.0%
0.88571428578
 
3.9%
0.87142857141
 
0.5%
0.85714285713
 
1.5%
0.82857142865
 
2.4%
0.77857142862
 
1.0%
0.771428571423
11.2%
0.76428571431
 
0.5%
0.75714285711
 
0.5%

horsepower
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2338211382
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:45.097092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05833333333
Q10.09166666667
median0.1958333333
Q30.2833333333
95-th percentile0.5533333333
Maximum1
Range1
Interquartile range (IQR)0.1916666667

Descriptive statistics

Standard deviation0.1647673617
Coefficient of variation (CV)0.7046726526
Kurtosis2.68400616
Mean0.2338211382
Median Absolute Deviation (MAD)0.1041666667
Skewness1.405310154
Sum47.93333333
Variance0.02714828348
MonotonicityNot monotonic
2022-09-01T21:03:45.268968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0833333333319
 
9.3%
0.0916666666711
 
5.4%
0.087510
 
4.9%
0.28333333339
 
4.4%
0.25833333338
 
3.9%
0.19583333337
 
3.4%
0.2756
 
2.9%
0.46666666676
 
2.9%
0.22083333336
 
2.9%
0.058333333336
 
2.9%
Other values (49)117
57.1%
ValueCountFrequency (%)
01
 
0.5%
0.016666666672
 
1.0%
0.029166666671
 
0.5%
0.033333333332
 
1.0%
0.041666666671
 
0.5%
0.051
 
0.5%
0.058333333336
 
2.9%
0.066666666671
 
0.5%
0.0833333333319
9.3%
0.087510
4.9%
ValueCountFrequency (%)
11
 
0.5%
0.89166666671
 
0.5%
0.66253
1.5%
0.63333333331
 
0.5%
0.56666666672
1.0%
0.55833333333
1.5%
0.53333333332
1.0%
0.52916666671
 
0.5%
0.4752
1.0%
0.47083333332
1.0%

citympg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3394308943
Minimum0
Maximum1
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:45.422952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.08333333333
Q10.1666666667
median0.3055555556
Q30.4722222222
95-th percentile0.6666666667
Maximum1
Range1
Interquartile range (IQR)0.3055555556

Descriptive statistics

Standard deviation0.181726157
Coefficient of variation (CV)0.5353848459
Kurtosis0.5786483405
Mean0.3394308943
Median Absolute Deviation (MAD)0.1388888889
Skewness0.6637040288
Sum69.58333333
Variance0.03302439615
MonotonicityNot monotonic
2022-09-01T21:03:45.579190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.528
13.7%
0.166666666727
13.2%
0.305555555622
10.7%
0.388888888914
 
6.8%
0.111111111113
 
6.3%
0.361111111112
 
5.9%
0.277777777812
 
5.9%
0.22222222228
 
3.9%
0.33333333338
 
3.9%
0.47222222228
 
3.9%
Other values (19)53
25.9%
ValueCountFrequency (%)
01
 
0.5%
0.027777777782
 
1.0%
0.055555555563
 
1.5%
0.083333333336
 
2.9%
0.111111111113
6.3%
0.13888888893
 
1.5%
0.166666666727
13.2%
0.19444444443
 
1.5%
0.22222222228
 
3.9%
0.254
 
2.0%
ValueCountFrequency (%)
11
 
0.5%
0.94444444441
 
0.5%
0.88888888891
 
0.5%
0.69444444447
3.4%
0.66666666676
2.9%
0.63888888891
 
0.5%
0.61111111111
 
0.5%
0.58333333331
 
0.5%
0.55555555561
 
0.5%
0.52777777781
 
0.5%

highwaympg
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3881899872
Minimum0
Maximum1
Zeros2
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:45.767130image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1578947368
Q10.2368421053
median0.3684210526
Q30.4736842105
95-th percentile0.7052631579
Maximum1
Range1
Interquartile range (IQR)0.2368421053

Descriptive statistics

Standard deviation0.1812221877
Coefficient of variation (CV)0.4668389027
Kurtosis0.4400703815
Mean0.3881899872
Median Absolute Deviation (MAD)0.1315789474
Skewness0.5399971879
Sum79.57894737
Variance0.0328414813
MonotonicityNot monotonic
2022-09-01T21:03:46.320710image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.236842105319
 
9.3%
0.578947368417
 
8.3%
0.210526315817
 
8.3%
0.368421052616
 
7.8%
0.421052631616
 
7.8%
0.473684210514
 
6.8%
0.552631578913
 
6.3%
0.315789473713
 
6.3%
0.342105263210
 
4.9%
0.44736842119
 
4.4%
Other values (20)61
29.8%
ValueCountFrequency (%)
02
 
1.0%
0.026315789471
 
0.5%
0.052631578952
 
1.0%
0.078947368422
 
1.0%
0.10526315792
 
1.0%
0.15789473688
3.9%
0.18421052637
 
3.4%
0.210526315817
8.3%
0.236842105319
9.3%
0.26315789473
 
1.5%
ValueCountFrequency (%)
11
 
0.5%
0.97368421051
 
0.5%
0.89473684211
 
0.5%
0.81578947372
 
1.0%
0.78947368422
 
1.0%
0.71052631584
 
2.0%
0.68421052633
 
1.5%
0.65789473683
 
1.5%
0.60526315792
 
1.0%
0.578947368417
8.3%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct189
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13276.71057
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2022-09-01T21:03:46.559134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6197
Q17788
median10295
Q316503
95-th percentile32472.4
Maximum45400
Range40282
Interquartile range (IQR)8715

Descriptive statistics

Standard deviation7988.852332
Coefficient of variation (CV)0.6017192504
Kurtosis3.051647871
Mean13276.71057
Median Absolute Deviation (MAD)3306
Skewness1.777678156
Sum2721725.667
Variance63821761.58
MonotonicityNot monotonic
2022-09-01T21:03:46.827348image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89212
 
1.0%
92792
 
1.0%
78982
 
1.0%
8916.52
 
1.0%
77752
 
1.0%
88452
 
1.0%
72952
 
1.0%
76092
 
1.0%
66922
 
1.0%
62292
 
1.0%
Other values (179)185
90.2%
ValueCountFrequency (%)
51181
0.5%
51511
0.5%
51951
0.5%
53481
0.5%
53891
0.5%
53991
0.5%
54991
0.5%
55722
1.0%
60951
0.5%
61891
0.5%
ValueCountFrequency (%)
454001
0.5%
413151
0.5%
409601
0.5%
370281
0.5%
368801
0.5%
360001
0.5%
355501
0.5%
350561
0.5%
341841
0.5%
340281
0.5%

Interactions

2022-09-01T21:03:38.446418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:15.449556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:17.444599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.150429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:20.931094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:23.830642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:25.549819image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:29.339404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:31.113554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:33.134428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:35.687557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:38.665895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:15.654260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:17.651056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.296231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:21.198632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:23.990508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:25.683463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:29.584277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:31.315220image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:33.291105image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:35.867378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:38.878718image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:15.800709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:17.832766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.433776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:21.514074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:24.166996image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:25.850125image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:29.764486image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:31.531925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:33.432135image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:36.084756image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:39.128609image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:15.966175image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:17.994823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.589170image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:21.715454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:24.330377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:26.014117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:29.929546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:31.683176image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:33.573342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:36.376882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:39.316294image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:16.167007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:18.129251image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.738947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:22.123490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:24.465815image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:26.216714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.083823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:31.865785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:33.716804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:36.679534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:39.513116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:16.351185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:18.265847image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.879317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:22.447879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:24.633519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:26.665026image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.215703image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:32.126970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:33.848701image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:37.039853image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:39.715198image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:16.534673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:18.414491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:20.012318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:22.630380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:24.792521image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:26.879432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.357539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:32.301300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:34.033826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:37.343445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:39.899544image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:16.699933image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:18.568245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:20.165310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:23.054650image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:24.997661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:27.066919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.490276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:32.449758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:34.217003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:37.584269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:40.048497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:16.922890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:18.730344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:20.333917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:23.370698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:25.132673image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:27.384522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.631953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:32.582319image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:34.952939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:37.766642image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:40.232484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:17.071160image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:18.867129image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:20.576530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:23.526811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:25.265890image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:28.772003image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.781566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:32.733067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:35.115273image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:38.064628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:40.530763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:17.211792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:19.007016image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:20.717088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:23.667242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:25.399507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:29.060327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:30.922629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:32.958919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:35.266736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2022-09-01T21:03:38.263209image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2022-09-01T21:03:47.092995image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-01T21:03:47.311750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-01T21:03:47.514871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-01T21:03:47.716737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-01T21:03:40.930989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-01T21:03:41.348335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

namewheelbasecarlengthcarwidthcurbweightcylindernumberenginesizeboreratiohorsepowercitympghighwaympgprice
0alfa-romero giulia0.0583090.4134330.3166670.4111710.20.2603770.6642860.2625000.2222220.28947413495.000
1alfa-romero stelvio0.0583090.4134330.3166670.4111710.20.2603770.6642860.2625000.2222220.28947416500.000
2alfa-romero Quadrifoglio0.2303210.4492540.4333330.5178430.40.3433960.1000000.4416670.1666670.26315816500.000
3audi 100 ls0.3848400.5298510.4916670.3293250.20.1811320.4642860.2250000.3055560.36842113950.000
4audi 100ls0.3731780.5298510.5083330.5182310.30.2830190.4642860.2791670.1388890.15789517450.000
5audi fox0.3848400.5402990.5000000.3952680.30.2830190.4642860.2583330.1666670.23684215250.000
6audi 100ls0.5597670.7701490.9250000.5259890.30.2830190.4642860.2583330.1666670.23684217710.000
7audi 50000.5597670.7701490.9250000.5686580.30.2830190.4642860.2583330.1666670.23684218920.000
8audi 40000.5597670.7701490.9250000.6198600.30.2641510.4214290.3833330.1111110.10526323875.000
9audi 5000s (diesel)0.3760930.5537310.6333330.6070600.30.2641510.4214290.4666670.0833330.15789517859.167

Last rows

namewheelbasecarlengthcarwidthcurbweightcylindernumberenginesizeboreratiohorsepowercitympghighwaympgprice
195volvo 144ea0.5160350.711940.5750000.5996900.20.3018870.8857140.2750000.2777780.31578913415.0
196volvo 244dl0.5160350.711940.5750000.5612880.20.3018870.8857140.2750000.3055560.31578915985.0
197volvo 2450.5160350.711940.5750000.6027930.20.3018870.8857140.2750000.3055560.31578916515.0
198volvo 264gl0.5160350.711940.5750000.6039570.20.2603770.7714290.4750000.1111110.15789518420.0
199volvo diesel0.5160350.711940.5750000.6474010.20.2603770.7714290.4750000.1111110.15789518950.0
200volvo 145e (sw)0.6559770.711940.7166670.5678820.20.3018870.8857140.2750000.2777780.31578916845.0
201volvo 144ea0.6559770.711940.7083330.6055080.20.3018870.8857140.4666670.1666670.23684219045.0
202volvo 244dl0.6559770.711940.7166670.5911560.40.4226420.7428570.3583330.1388890.18421121485.0
203volvo 2460.6559770.711940.7166670.6706750.40.3169810.3357140.2416670.3611110.28947422470.0
204volvo 264gl0.6559770.711940.7166670.6105510.20.3018870.8857140.2750000.1666670.23684222625.0